Let’s say your e-commerce company ships a significant amount of orders to customers. At least 10% of them are returned because the address on the label is incorrect or incomplete. Such returns are costly for the company if the frequency of errors is constant. In cases like this, where the accuracy of information is critical for customer and supplier management, it is essential to have standardized data .
Database normalization is a process that largely requires unification of criteria for the collection and processing of information, as well as a similar amount of attention and patience. In the following lines, we will discuss the phases of the procedure required to obtain correct, enriched and duplicate-free data. We will also mention the benefits generated by this practice.
What is the process for obtaining normalized data?
In essence, data normalization is a process of organizing databases by applying a set of rules to clean up hat & bag image editing structure. The purpose of the procedure is to remove unnecessary duplications and dependencies from data tables and their related tables.
It is worth remembering that duplicate data is data generated by multiple users adding data to the database at the same time. But it also appears in databases whose design does not include duplicate detection. Unnecessary dependencies are relationships that should not exist between data. An example of this would be finding dependent qualifiers from third-party or temporary tables in an organization’s tax information record.
This may require creating new tables and establishing relationships between them following rules designed both to protect the data and to obtain a much more flexible database after removing redundancies and dependencies.
Obviously, duplicate data takes up more space on the memory disk and in cloud storage . Apart from that, it can cause maintenance problems. When making changes to data present in multiple locations, these must be done exactly the same in each of them.
As an illustration, achieving normalized data for the current customer portfolio would allow temporary indicators to be eliminated from the record, for example, non-essential historical data. It is also possible to discard data that depends on third tables.
In particular, accurately assigning the data value is very important as this will be the only way to ensure the betting email list of duplicates. Consequently, changes will be made to the data and will be accurately cross-referenced.
Phases or levels to be met to obtain standardized data
There are actually several phases or levels of normalization applicable to databases. However, only three are the most common in organizations and are called “normal forms.” Each includes standards and criteria that establish the degree of vulnerability of the information to possible errors and inconsistencies. Generally, data Jinsi ya kutengeneza T-shirt ya Kipekee kwa Watoto Wako follows all three normal forms required for most applications are considered to be normalized at the highest level. These levels are briefly described below.
First normal form
To complete this first phase, you must do the following:
First, remove duplicate data groups from individual tables.
For each group of related data, it is essential to create a separate table.
Assigns a primary key to each group of related data, with no null attributes.
Avoid using multiple fields in the same table to store similar data.
It is also important not to include data with identical meanings in the same table. You should also make sure that the attributes are minimal and indivisible and that the rows and columns are clearly independent. This will prevent a possible change in order from modifying their meaning.
Second normal form
At this point, you need to consider managing multiple records. In other words, if a data set applies to multiple records, it is advisable to create separate tables and relate them to each other with a foreign key.
